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Article

Automated Machine Learning Pipeline for Traffic Count Prediction

1
Department of Civil and Environmental Engineering, University of Central Florida, Orlando, FL 32816, USA
2
Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
3
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
4
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Miquel Sànchez-Marrè FiEMSs
Modelling 2021, 2(4), 482-513; https://doi.org/10.3390/modelling2040026
Received: 19 July 2021 / Revised: 29 September 2021 / Accepted: 29 September 2021 / Published: 12 October 2021
Research indicates that the projection of traffic volumes is a valuable tool for traffic management. However, few studies have examined the application of a universal automated framework for car traffic volume prediction. Within this limited literature, studies using broad data sets and inclusive predictors have been inadequate; such works have not incorporated a comprehensive set of linear and nonlinear algorithms utilizing a robust cross-validation approach. The proposed model pipeline introduced in this study automatically identifies the most appropriate feature-selection method and modeling approach to reduce the mean absolute percentage error. We utilized hyperparameter optimization to generate a universal automated framework, distinct from model optimization techniques that rely on a single case study. The resulting model can be independently customized to any respective project. Automating much of this process minimizes the work and expertise required for traffic count forecasting. To test the applicability of our models, we used Florida historical traffic data from between 2001 and 2017. The results confirmed that nonlinear models outperformed linear models in predicting passenger vehicles’ monthly traffic volumes in this specific case study. By employing the framework developed in this study, transportation planners could identify the critical links on US roads that incur overcapacity issues. View Full-Text
Keywords: machine learning; passenger vehicle traffic; traffic volume; predictive modeling; regression analysis machine learning; passenger vehicle traffic; traffic volume; predictive modeling; regression analysis
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MDPI and ACS Style

Mahdavian, A.; Shojaei, A.; Salem, M.; Laman, H.; Yuan, J.-S.; Oloufa, A. Automated Machine Learning Pipeline for Traffic Count Prediction. Modelling 2021, 2, 482-513. https://doi.org/10.3390/modelling2040026

AMA Style

Mahdavian A, Shojaei A, Salem M, Laman H, Yuan J-S, Oloufa A. Automated Machine Learning Pipeline for Traffic Count Prediction. Modelling. 2021; 2(4):482-513. https://doi.org/10.3390/modelling2040026

Chicago/Turabian Style

Mahdavian, Amirsaman, Alireza Shojaei, Milad Salem, Haluk Laman, Jiann-Shiun Yuan, and Amr Oloufa. 2021. "Automated Machine Learning Pipeline for Traffic Count Prediction" Modelling 2, no. 4: 482-513. https://doi.org/10.3390/modelling2040026

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